What revolutionary technique did DeepMind use in their AI advancement?

The choice that highlights a revolutionary technique used by DeepMind relates to their innovative approach of incorporating visual data into their AI systems, notably in the context of their work with the game Breakout and other Atari games. By passing in screenshots of states into a neural network and using that information to predict Q-values (the expected future rewards for taking certain actions in specific states), DeepMind developed a more effective method for learning optimal strategies.

This method allowed the AI to interpret raw pixel data directly, enabling it to learn from visual inputs rather than relying on pre-defined features or structured data. This advancement showcased the power of deep learning techniques in reinforcement learning frameworks, thus significantly improving the ability of AI to learn in complex environments through trial and error, similar to how humans learn through experiences.

The other options present valid concepts but do not capture the defining and revolutionary essence of DeepMind's advancements as effectively as this choice does. For instance, while reinforcement learning is a critical part of the process, it is the combination of reinforcement learning with the neural network that represents the innovation. Similarly, while Q-learning and hybrid models are essential components of AI, they don’t showcase the unique ability of the system to learn from unstructured visual data in the same groundbreaking way.